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All Journal Bulletin of Electrical Engineering and Informatics Techno: Jurnal Penelitian Jurnal Teknologi Informasi dan Ilmu Komputer JUSIFO : Jurnal Sistem Informasi PROtek : Jurnal Ilmiah Teknik Elektro IJIS - Indonesian Journal On Information System Jiko (Jurnal Informatika dan komputer) Patria Artha Technological Journal Jurnal Teknik Informatika Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Dialektika Informatika (Detika) BAKTI : Jurnal Pengabdian Kepada Masyarakat RENATA Jurnal Pengabdian Masyarakat Kita Semua BAKTI : Jurnal Pengabdian Kepada Masyarakat
Tempola, Firman
Universitas Khairun
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Transportation Other

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The idealPosition System: Sebagai Solusi Pendukung Keputusan untuk Menentukan Pemain Bola yang Ideal Berdasarkan Posisi Pemain Faisal F Taran; Abdul Mubarak; Firman Tempola; Achmad Fuad; Salkin Lutfi
JUSIFO : Jurnal Sistem Informasi Vol 6 No 2 (2020): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v6i2.6468

Abstract

In North Maluku, especially in Ternate, many football schools have been opened to train and find potential Indonesian National players. One of them has contributed to producing Indonesian National player candidates, namely Sekolah Sepak Bola (SSB) Tunas Gamalama. The problems that occur at this time at SSB Tunas Gamalama, managers and coaches are difficult to determine the ideal player to fill each position. Oftentimes, SSB Tunas Gamalama students choose a position according to their idol football players, and also because of the popularity of these positions. The tendency of SSB Tunas Gamalama students to be like this results in an imbalance of potential players in certain position. During this time the coach takes a long time and is often subjective in selecting players at every position available. In this research, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used as a method of decision support. This article aims to build a Decision Support System (DSS) in determining the ideal football player based on the player's position (The idealPosition System) using the TOPSIS method. This research produces DSS which can be used to determine the ideal soccer player based on the player's position.
Classification of clove types using convolution neural network algorithm with optimizing hyperparamters Firman Tempola; Retantyo Wardoyo; Aina Musdholifah; Rosihan Rosihan; Lilik Sumaryanti
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5533

Abstract

This study uses clove imagery by classifying it according to ISO 2254-2004 standards: whole, headless, and mother clove. This type of clove will affect the quality and economic value when it has been dried. For this reason, it is necessary to take a first step to control cloves' quality. One way is to classify it from the start. This research will utilize the convolution neural network algorithm and compare it with model transfer learning and modified VGG16 architecture on clove images. In addition, research is also looking for the most optimal hyperparameter. The results of this study indicate that the application of convolution neural network (CNN) to clove images obtains an accuracy value of 84% using a hyperparameter of 50 epochs, a learning rate of 0.001, and a batch size of 16. Meanwhile, for the application of transfer learning VGG16, Resnet50, MobileNetV2, InceptionV3, DensetNet151, and modified VGG16 have respectively each of the highest accuracy including 95.70%, 76.15%, 96.89%, 98.07%, 98.96%, and 99.11%.
Co-Authors Abd Hakim Husen Abdul Mubarak Abdul Mubarak ACHMAD FUAD Achmad Fuad1 Aina Musdholifah Akbar Riodano Laisouw Amal Khairan Amal Khairan Anas Anas Anas Anas, Anas Andi Sitti Nur Afiah Assaf Arief, Assaf Azim Hisyam Baksir Faisal F Taran Ibnu, Wirda Sintia Ishak, Gunawan Iwan La Udin Jamil, Moh. Kapita, Syarifuddin N Latif, Nurdewi Lilik Sumaryanti Moh. Jamil Mubarak, Abdul Muhammad Sabri Ahmad Muhammad, Miftah Munazat Salmin Putra, Surya Adi Pratama Ranjasmara, Muhammad Rendy Pratama Retantyo Wardoyo Rosihan Rosihan Rosihan, Rosihan rosihan, rosihan Saiful Do Abdullah Salkin Lutfi Salkin Lutfi Samlan Mahdi Sirajuddin, Hairil Kurniadi Soesanti Soesanti Sujabtri Kabau, Haan Sukmawati, Hesti Sukril I Wahid Syarifuddin N. Kapita Umar, Kiki Amelia Wahid, Sukril I